Integration of sensor data with AI for personalised health interventions

  Wearable sensor data can be processed by machine learning (ML) and artificial intelligence (AI) algorithms to yield insights into a person's health status. This allows for the early identification of health problems and the delivery of individualised healthcare. Healthcare providers can create individualised treatment regimens that take into account each patient's unique traits with the help of AI algorithms. Based on particular facts, treatment plans may include recommendations for therapy, personalised drug dosages, and lifestyle changes. In order to anticipate and avoid machine issues, artificial intelligence (AI) processes and analyses data from sensors, including temperature, thermal, ultrasonic, photocell, inductive, radar, LiDAR, vision, and motion sensors. Artificial intelligence (AI) algorithms enhance system precision by gaining the ability to comprehend training data. This, in turn, facilitates humans in gaining unparalleled insights into treatment variability, care processes, diagnostics, and patient outcomes. 

 

  AI can assist doctors in developing individualised treatment programs for each patient that take into account their particular medical background, genetic composition, lifestyle choices, and other aspects. Both patient outcomes and healthcare costs may be improved by this focused strategy. Artificial intelligence (AI) is driving the creation of adaptive learning platforms, which use student performance data analysis to generate individualised learning plans. These systems adjust the content delivery and difficulty level in real-time based on their understanding of each student's learning style through the use of machine learning algorithms. AI, for instance, uses sensory elements like cameras and temperature sensors to examine the surroundings. One use of AI perception is autonomous driving. By detecting and measuring a wide range of characteristics, including temperature, pressure, humidity, flow rate, motion, and position, sensors perform a critical function. They make production smarter and automated by converting physical signals into electrical signals and giving the control system information in real time. In robotic systems, sensors supply essential sensory data. Position, size, direction, velocity, distance, temperature, weight, force, and many other variables that let robots perceive their surroundings and perform tasks are included in these data. AI is being used in radiology to find anomalies in pictures from CT, MRI, and X-ray scans. 

 

  In pathology, artificial intelligence (AI) is used to identify and categorise various tissue types by analysing biopsy slides and microscope images. In order to detect indicators of lung nodules, breast cancer, and many other illnesses earlier and more efficiently than before, AI is assisting clinicians in the analysis of images. By evaluating large patient datasets to suggest individualised treatment regimens, optimise medicine dosages, and anticipate possible bad effects, generative AI may also assist physicians in improving patient care. It enables medical providers to make data-driven choices, enhancing diagnostic precision and customising care for each patient. The potential advantages of AI in customised medicine are enormous, even though there are still obstacles to be addressed, such as prejudice and data protection. In the end, generative AI can be a useful tool to enhance human-machine interaction in medicine, but it cannot completely replace it. Contributions are invited from a range of disciplines and perspectives, including, but not restricted to: Integration of sensor data with AI for personalised health interventions. 



Potential topics include but are not limited to the following:

 

  • Adaptive and customised health and wellness systems using machine learning approaches.
  • Medical sensors with artificial intelligence to support clinical judgments.
  • An overview of data science and AI tools for managing cancer.
  • Artificial intelligence of the future for customised healthcare.
  • Personalised medicine uses AI techniques for data processing and utilisation.
  • Wearable and mobile sensors to enable customised feeding.
  • Machine learning for customised preventive adolescent health care.
  • Medical embedded technology for customised treatment.
  • Limitations in individualised nutrition and health care.
  • Integrating wearable sensor data and artificial intelligence to identify and forecast cardiovascular disease.
  • Wearable technologies, blockchain, and artificial intelligence combined for the management of chronic illnesses.
  • Wearable sensors for monitoring and analysis of data in the medical field.



Details of the Our Guest Editor Team:  

Prof. Usman Ahmad Usmani

Assistant Professor,

Universiti Teknologi Petronas,

Seri Iskandar, Malaysia.

Official Email: [email protected] , [email protected] 

Google Scholar: https://scholar.google.co.in/citations?user=fPSHmRIAAAAJ&hl=en 

 

Prof. Junzo Watada

Professor,

Faculty of Data Science, 

Shimonoseki City University,

Shimonoseki, Yamaguchi 751-8510, Japan

Official Email: [email protected] 

Official Page: https://w-rdb.waseda.jp/html/100002838_en.html#item_kenkyu_keyword_2 

Google Scholar: https://scholar.google.com/citations?user=pU-e-HoAAAAJ&hl=en 

 

Dr. Abdulla Yousuf Usmani, 

Assistant Professor,

Aligarh Muslim University, 

Uttar Pradesh, India.

Official Email: [email protected] 

Official Page: https://www.amu.ac.in/faculty/mechanical-engineering/abdullah-yousuf-usmani 

Google Scholar: https://scholar.google.co.in/citations?user=_vND8fwAAAAJ&hl=en 

 

Tentative timeline for organising this special issue:

Submission Deadline - 30.11.2024

Authors Notification - 25.02.2025

Revised Papers Due - 15.04.2025

Final notification - 25.06.2025